1. Technical Field
Embodiments disclosed herein relate to text analytics. More specifically, embodiments provide techniques for generating culinary recipes by applying text analytics techniques on unstructured recipe texts.
2. Description of the Related Art
Text information is often voluminous and unstructured. To use large amounts of text information for a particular purpose, the information often needs to be structured based on its language and content. Text analytics describes a set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources. Such techniques process the text information to identify structures, common meanings, and relationships between different words and word phrases. Through these techniques, organizations and individuals alike may extract value from the processed information. As more content becomes available on the Internet, text analytics becomes of increasing importance.
Text analytics has applications in a variety of contexts. For example, consider culinary recipes, which are increasingly shared over the Internet through recipe websites, food blogs, and the like. Many culinary recipes may describe a variety of ingredients and methods for preparing a given dish. For example, one recipe for a pasta dish may specify a different set of herbs compared to another recipe. Further, one recipe may specify a different preparation method compared to another recipe (e.g., boiling or baking the pasta dish). Generally, when an individual searches online for a recipe for a particular dish, the individual may receive hundreds of results. Because the results are numerous, the individual often may consider only the first few hits and disregard the rest, without any regard to the quality of the remaining recipes. Additionally, an individual may have specific dietary needs, so the individual might have difficulty obtaining a recipe that suits these needs.